May 14, 2024, 4:44 a.m. | Yuheng Jia, Jiawei Tang, Jiahao Jiang

cs.LG updates on arXiv.org arxiv.org

arXiv:2303.06847v2 Announce Type: replace
Abstract: Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. However, annotating label distribution (LD) for training samples is extremely costly. So recent studies often first use label enhancement (LE) to generate the estimated label distribution from the logical label and then apply external LDL algorithms on the recovered label distribution to predict the label distribution for unseen samples. But this step-wise manner overlooks the …

abstract arxiv cs.ai cs.lg distribution generate however replace sample samples studies training type

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